NETSE: Small: Towards Better Modeling of Communication Activity Dynamics in Large-Scale Online Social Networks

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Online social networks (OSNs) have witnessed tremendous growth and popularity over the recent years. The huge success and increasing popularity of social networks makes it important to characterize and study their behavior in detail. Recent work in analyzing online social network data has focused primarily on either static social network structure or evolving social networks. However, popular OSNs sites provide mechanisms to form and maintain community over time by facilitating communication, content sharing, and other forms of activities. This research will develop a suite of algorithmic and analytic methods to support the characterization and modeling of activity networks. In particular, we will conduct static and temporal characterization studies of social network activity, study sampling techniques that can preserve graph properties for different communication activity graphs, investigate the fundamental theoretical trade-offs between preserving different properties of the graph, and develop procedural modeling techniques to generate social network activity graphs to better represent the temporal dynamics and burstiness of activity patterns.We firmly believe that the insights garnered from the proposed algorithm development and theoretical analysis will have a significant impact on sampling and analysis in other network-centric domains in addition to OSNs. All the ideas that come out of the research will be incorporated into both graduate as well as undergraduate level networking and data-mining courses.